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Update Harris Corner Detection.cpp
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‎Feature Extraction/Harris Corner Detection.cpp

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#include "opencv2/opencv.hpp"
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#include "opencv2/imgproc/imgproc.hpp"
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#include "iostream"
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#include "algorithm"
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#include "vector"
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#include "stdio.h"
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#include "map"
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#include "unordered_map"
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using namespace std;
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using namespace cv;
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Mat RGB2GRAY(const Mat &src){
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if(!src.data || src.channels()!=3){
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exit(1);
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}
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int row = src.rows;
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int col = src.cols;
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Mat gray = Mat(row, col, CV_8UC1);
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for(int i = 0; i < row; i++){
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for(int j = 0; j < col; j++){
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gray.at<uchar>(i, j) = (uchar)(0.114 * src.at<Vec3b>(i, j)[0] + 0.587 * src.at<Vec3b>(i, j)[1] + 0.299 * src.at<Vec3b>(i, j)[2]);
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}
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}
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return gray;
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}
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void SobelGradDirction(Mat &src, Mat &sobelX, Mat &sobelY){
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int row = src.rows;
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int col = src.cols;
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sobelX = Mat::zeros(src.size(), CV_32SC1);
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sobelY = Mat::zeros(src.size(), CV_32SC1);
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for(int i = 0; i < row-1; i++){
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for(int j = 0; j < col-1; j++){
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double gradY = src.at<uchar>(i+1, j-1) + src.at<uchar>(i+1, j) * 2 + src.at<uchar>(i+1, j+1) - src.at<uchar>(i-1, j-1) - src.at<uchar>(i-1, j) * 2 - src.at<uchar>(i-1, j+1);
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sobelY.at<float>(i, j) = abs(gradY);
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double gradX = src.at<uchar>(i-1, j+1) + src.at<uchar>(i, j+1) * 2 + src.at<uchar>(i+1, j+1) - src.at<uchar>(i-1, j-1) - src.at<uchar>(i, j-1) * 2 - src.at<uchar>(i+1, j-1);
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sobelX.at<float>(i, j) = abs(gradX);
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}
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}
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//将梯度数组转换为8位无符号整形
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convertScaleAbs(sobelX, sobelX);
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convertScaleAbs(sobelY, sobelY);
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}
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Mat SobelXX(const Mat src){
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int row = src.rows;
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int col = src.cols;
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Mat_<float> dst(row, col, CV_32FC1);
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for(int i = 0; i < row; i++){
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for(int j = 0; j < col; j++){
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dst.at<float>(i, j) = src.at<uchar>(i, j) * src.at<uchar>(i, j);
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}
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}
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return dst;
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}
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Mat SobelYY(const Mat src){
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int row = src.rows;
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int col = src.cols;
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Mat_<float> dst(row, col, CV_32FC1);
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for(int i = 0; i < row; i++){
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for(int j = 0; j < col; j++){
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dst.at<float>(i, j) = src.at<uchar>(i, j) * src.at<uchar>(i, j);
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}
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}
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return dst;
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}
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Mat SobelXY(const Mat src1, const Mat &src2){
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int row = src1.rows;
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int col = src1.cols;
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Mat_<float> dst(row, col, CV_32FC1);
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for(int i = 0; i < row; i++){
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for(int j = 0; j < col; j++){
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dst.at<float>(i, j) = src1.at<uchar>(i, j) * src2.at<uchar>(i, j);
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}
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}
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return dst;
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}
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void separateGaussianFilter(Mat_<float> &src, Mat_<float> &dst, int ksize, double sigma){
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CV_Assert(src.channels()==1 || src.channels() == 3); //只处理单通道或者三通道图像
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//生成一维的
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double *matrix = new double[ksize];
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double sum = 0;
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int origin = ksize / 2;
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for(int i = 0; i < ksize; i++){
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double g = exp(-(i-origin) * (i-origin) / (2 * sigma * sigma));
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sum += g;
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matrix[i] = g;
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}
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for(int i = 0; i < ksize; i++) matrix[i] /= sum;
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int border = ksize / 2;
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copyMakeBorder(src, dst, border, border, border, border, BORDER_CONSTANT);
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int channels = dst.channels();
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int rows = dst.rows - border;
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int cols = dst.cols - border;
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//水平方向
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for(int i = border; i < rows - border; i++){
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for(int j = border; j < cols - border; j++){
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float sum[3] = {0};
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for(int k = -border; k<=border; k++){
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if(channels == 1){
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sum[0] += matrix[border + k] * dst.at<float>(i, j+k);
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}else if(channels == 3){
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Vec3f rgb = dst.at<Vec3f>(i, j+k);
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sum[0] += matrix[border+k] * rgb[0];
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sum[1] += matrix[border+k] * rgb[1];
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sum[2] += matrix[border+k] * rgb[2];
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}
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}
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for(int k = 0; k < channels; k++){
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if(sum[k] < 0) sum[k] = 0;
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else if(sum[k] > 255) sum[k] = 255;
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}
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if(channels == 1)
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dst.at<float>(i, j) = static_cast<float>(sum[0]);
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else if(channels == 3){
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Vec3f rgb = {static_cast<float>(sum[0]), static_cast<float>(sum[1]), static_cast<float>(sum[2])};
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dst.at<Vec3f>(i, j) = rgb;
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}
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}
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}
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//竖直方向
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for(int i = border; i < rows - border; i++){
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for(int j = border; j < cols - border; j++){
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float sum[3] = {0};
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for(int k = -border; k<=border; k++){
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if(channels == 1){
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sum[0] += matrix[border + k] * dst.at<float>(i+k, j);
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}else if(channels == 3){
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Vec3f rgb = dst.at<Vec3f>(i+k, j);
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sum[0] += matrix[border+k] * rgb[0];
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sum[1] += matrix[border+k] * rgb[1];
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sum[2] += matrix[border+k] * rgb[2];
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}
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}
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for(int k = 0; k < channels; k++){
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if(sum[k] < 0) sum[k] = 0;
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else if(sum[k] > 255) sum[k] = 255;
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}
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if(channels == 1)
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dst.at<float>(i, j) = static_cast<float>(sum[0]);
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else if(channels == 3){
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Vec3f rgb = {static_cast<float>(sum[0]), static_cast<float>(sum[1]), static_cast<float>(sum[2])};
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dst.at<Vec3f>(i, j) = rgb;
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}
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}
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}
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delete [] matrix;
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}
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Mat harrisResponse(Mat_<float> GaussXX, Mat_<float> GaussYY, Mat_<float> GaussXY, float k){
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int row = GaussXX.rows;
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int col = GaussXX.cols;
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Mat_<float> dst(row, col, CV_32FC1);
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for(int i = 0; i < row; i++){
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for(int j = 0; j < col; j++){
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float a = GaussXX.at<float>(i, j);
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float b = GaussYY.at<float>(i, j);
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float c = GaussXY.at<float>(i, j);
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dst.at<float>(i, j) = a * b - c * c - k * (a + b) * (a + b);
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}
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}
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return dst;
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}
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int dir[8][2] = {0, 1, 0, -1, 1, 0, -1, 0, 1, 1, 1, -1, -1, 1, -1, -1};
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void MaxLocalValue(Mat_<float>&resultData, Mat srcGray, Mat &resultImage, int kSize){
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int r = kSize / 2;
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resultImage = srcGray.clone();
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int row = resultImage.rows;
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int col = resultImage.cols;
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for(int i = r; i < row - r; i++){
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for(int j = r; j < col - r; j++){
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bool flag = true;
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for(int k = 0; k < 8; k++){
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int tx = i + dir[k][0];
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int ty = j + dir[k][1];
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if(resultData.at<float>(i, j) < resultData.at<float>(tx, ty)){
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flag = false;
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}
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}
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if(flag && (int)resultData.at<float>(i, j) > 18000){
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circle(resultImage, Point(i, j), 5, Scalar(0, 0, 255), 2, 8, 0);
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}
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}
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}
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}
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int main(){
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Mat src = imread("../lena.jpg");
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Mat srcGray = RGB2GRAY(src);
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Mat imageSobelX;
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Mat imageSobelY;
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Mat resultImage;
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Mat_<float> imageSobelXX, imageSobelYY, imageSobelXY;
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Mat_<float> GaussianXX, GaussianXY, GaussianYY, HarrisResponse;
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SobelGradDirction(srcGray, imageSobelX, imageSobelY);
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imageSobelXX = SobelXX(imageSobelX);
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imageSobelYY = SobelYY(imageSobelY);
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imageSobelXY = SobelXY(imageSobelX, imageSobelY);
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separateGaussianFilter(imageSobelXX, GaussianXX, 3, 1.0);
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separateGaussianFilter(imageSobelYY, GaussianYY, 3, 1.0);
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separateGaussianFilter(imageSobelXY, GaussianXY, 3, 1.0);
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HarrisResponse = harrisResponse(GaussianXX, GaussianYY, GaussianXY, 0.05);
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MaxLocalValue(HarrisResponse, srcGray, resultImage, 3);
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imshow("origin", src);
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imshow("gray", srcGray);
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imshow("result", resultImage);
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imwrite("../result.jpg", resultImage);
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waitKey(0);
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return 0;
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}

‎Feature Extraction/README.md

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# OpenCV和C++实现图像特征提取的一些算法
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- Sobel Edge Detection.cpp C++实现了OpenCV中的Sobel边缘检测算法。算法原理请看：https://blog.csdn.net/just_sort/article/details/85015054
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- Canny Edge Detection.cpp C++实现了OpenCV中的Canny边缘检测算法，相对Sobel算子可以提取更细，更可靠的边缘。算法原理请看：https://blog.csdn.net/just_sort/article/details/85053157
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- Canny Edge Detection.cpp C++实现了OpenCV中的Canny边缘检测算法，相对Sobel算子可以提取更细，更可靠的边缘。算法原理请看：https://blog.csdn.net/just_sort/article/details/85053157
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- Harris Corner Detection.cpp 实现了Opencv中的角点检测算法。原理请看：https://blog.csdn.net/just_sort/article/details/85302341

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